Bridging science communication and open science—Working inclusively toward the common good
Bibliographic record
Abstract
The 2020–2022 pandemic highlighted concerns about “information disorders”, pressing for approaches capable of guiding the science-society alliance toward a mutually beneficial direction. This essay advocates for and presents a framework proposing the combination of Open Science (OS) and Science Communication (SciComm) practices. OS encourages public access to scientific material, while SciComm has historically enabled public understanding of scientific knowledge. Despite their similar goals, these two communities are disconnected. We draw on the concepts of “boundary object” and “epistemic trust” to demonstrate how this framework could foster a bond between scientific expertise and public reason toward an informed and inclusive common good. The OS-SciComm framework is based on the notion that ensuring transparency in science also requires “bridging tools” that deal with the complexity of scientific lexicon and processes. It values scientific expertise, but does not undermine citizens' capabilities in information processing and their interest in accessing scientific outputs. Our proposal also acknowledges controversies involving open scientific materials during the COVID-19 pandemic and advises caution when drawing conclusions from cases that are often context-specific. The OS-SciComm framework requires innovative ideas, platforms and actions. We invite both communities to join us in this endeavor.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.069 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.015 | 0.081 |
| Scholarly communication | 0.035 | 0.045 |
| Open science | 0.003 | 0.043 |
| Research integrity | 0.017 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".